Octo Telematics, a leader in telematics for insurance companies, is introducing innovations for insurance by aggregating 186 billion miles of driving data from connected cars and using Cloudera Enterprise to predict and model driver risk.

In this machine learning use case, we take a look at Technical Safety BC, an independent, self-funded organization that oversees the safe installation and operation of technical systems and equipment across the province of British Columbia in Canada. The not-for-profit organization recently partnered with data science software maker Dataiku to introduce more sophisticated machine autonomy to their hazard identification process. The partnership enables Technical Safety BC to build machine learning and advanced analytics-based solutions faster and more accurately, allowing the company to better target areas of high risk.

In an effort to continue to grow their business in existing and new markets, DAZN – a live and on-demand sports streaming service – wanted a fast, low-maintenance way to enable their small data team to run predictive analytics and machine learning projects at scale. In this case study, we’ll see how the company turned to Amazon Web Services (AWS) and Dataiku in combination for their simplicity in setup, connection, integration, and usability.

A major breakthrough in the FemTech domain has recently been done by Flo Period Tracker – the first period tracking app to publicly announce using artificial intelligence for improving cycle predictions. Flo became the most downloaded app worldwide in its category within months after introducing neural networks to its prediction algorithm.

The insideBIGDATA technology use case guide – Ticketmaster: Using the Cloud Capitalizing on Performance, Analytics, and Data to Deliver Insights provides an in-depth look at a high-profile cloud migration use case. One of the most widely discussed topics in IT today is moving workloads to cloud. The process of deciding whether or when to migrate to the cloud can be daunting. Further, finding the right technology fit for your enterprise objectives can be challenging with a cloud solution ecosystem filled with alternatives. To make the cloud adoption process more straightforward, this white paper provided a number of areas for consideration when evaluating a cloud platform.

EXASOL, a high-performance in-memory analytic database developer, and PATH, an international nonprofit organization and global leader in health and innovation, announced a partnership to support the Zambian government’s ambitious campaign to eliminate malaria by 2020.

Midwest Anesthesia Consultants is an association of over 100 healthcare providers who rely on the organization’s commitment to quality and comprehensive and compassionate care in the areas of anesthesia, critical care, and pain management. The organization’s extensive team of board certified anesthesiologists is fully integrated into the medical staff at every hospital campus location as members of care teams and governing boards.

The insideBIGDATA Guide to Healthcare & Life Sciences is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting new area of technology. This segment focuses on the use of distributed system architectures – Hadoop and Spark.

Industry Perspectives

In this special guest feature, Assaf Katan, CEO & Co-Founder of Apertio, the Open Data deep search engine, suggests that there are huge social and financial benefits that businesses and economies can realize if they can successfully leverage Open Data. Despite this, there are still some hurdles for data professionals to leap. A great way to start is to consider whether your data meets the criteria for what’s known as the FAIR principles. These are Findability, Accessibility, Interoperability and Reusability. [READ MORE…]

White Papers

The value and benefits of a data catalog are often described as the ability for analysts to find the data they need quickly and efficiently. Data cataloging accelerates analysis by minimizing the time and effort that analysts spend finding and preparing data.